Methods for automating aspects of machine learning, and related systems and apparatus

ABSTRACT

Various systems and methods provide an intuitive user interface that enables automatic specification of queries and constraints for analysis by ML component. Various implementations provide methodologies for automatically formulating machine learning (“ML”) and optimization queries. The automatic generation of ML and/or optimization queries can be configured to use examples to facilitate formulation of ML and optimization queries. One example method includes accepting input data specifying variables and data values associated with the variables. Within the input data any unspecified data records are identified, and a relationship between the variables specified in the input data and a variable associated with the at least one unspecified data record is automatically determined. The relationship can be automatically determined based on training data contained within the input data. Once a relationship is established a ML problem can be automatically generated.

RELATED APPLICATIONS

This application is a continuation of and claims priority under 35U.S.C. § 120 to U.S. patent application Ser. No. 15/351,002, entitled“SYSTEM AND METHOD FOR AUTO-QUERY GENERATION” and filed on Nov. 14,2016, which is a continuation of and claims priority under 35 U.S.C. §120 to U.S. patent application Ser. No. 14/202,780, entitled “SYSTEM ANDMETHOD FOR AUTO-QUERYGENERATION,” filed on Mar. 10, 2014 which is acontinuation-in-part and claims priority under 35 U.S.C. § 120 to U.S.patent application Ser. No. 14/016,287, entitled “SYSTEMS AND METHODSFOR DATA SET SUBMISSION, SEARCHING AND RETRIEVAL,” filed on Sep. 3,2013, which claims priority to U.S. Provisional Patent Application61/695,660, entitled “SYSTEMS AND METHODS FOR SYMBOLIC ANALYSIS BETWEENDATA SETS,” filed Aug. 31, 2012 and U.S. Provisional Patent Application61/695,637, entitled “SYSTEMS AND METHODS FOR DATA SET SUBMISSION,SEARCHING AND RETRIEVAL,” filed Aug. 31, 2012. U.S. patent applicationSer. No. 14/202,780 also is a continuation-in-part and claims priorityunder 35 U.S.C. § 120 to U.S. patent application Ser. No. 14/016,300entitled “SYSTEMS AND METHODS FOR SYMBOLIC ANALYSIS,” filed Sep. 3, 2013which claims priority to U.S. Provisional Patent Application 61/695,660,“SYSTEMS AND METHODS FOR SYMBOLIC ANALYSIS BETWEEN DATA SETS,” filed onAug. 31, 2012, and U.S. Provisional Application Ser. No. 61/695,637entitled “SYSTEMS AND METHODS FOR DATA SET SUBMISSION, SEARCHING ANDRETRIEVAL,” filed on Aug. 31, 2012. U.S. patent application Ser. No.14/202,780 also claims priority under 35 U.S.C. § 119 to U.S.Provisional Patent Application 61/779,451, entitled “SYSTEM AND METHODFOR AUTO-QUERY GENERATION,” filed on Mar. 13, 2013. Each of theabove-identified applications is incorporated herein by reference in itsentirety.

BACKGROUND

A major bottleneck in the use of advanced machine-learning (“ML”)techniques and tools today is that they require a substantial technicalskill level to operate. While data is becoming increasingly easy tocollect, store and manipulate for lay persons, analysis tools are notkeeping pace.

Many machine-learning (ML) algorithms exist today that can allow theirusers to automatically generate computational models for prediction andoptimization. For example, Neural networks, Support vector machines,decision trees, symbolic regression, and other techniques create amathematical model that can be applied to predict dependent values fromnew independent variables, based on examples (training data). Thesemodels can be used to predict values in static tables as well as indynamic time series. Vector equations that predict multiple valuessimultaneously (e.g., x, y coordinates) are also available.Conventionally, users specify a query that the ML system solves,delivering a model that can be used for the predictions, regression, orclassification of values.

In addition to predicting unknown dependent values, ML models can alsobe used for optimization. Search algorithms such as gradient ascent orglobal search algorithms such as Genetic Algorithms can be used tosearch for an optimal set of independent variables such that thedependent variable is maximized or minimized, or becomes as close aspossible to a desired value. Again, in order to interact with thesystem, the user specifies the boundaries of the optimization problemand any constraints that apply.

SUMMARY

According to one aspect, it is realized that ML techniques andapproaches are not meeting their potential. In particular, common usersfind the generation and development of a query for an ML system toochallenging. Lay persons and more novice users find the task of querydefinition overwhelming. Frustration can results from imprecise querygeneration, where “bad” answers are delivered in response to improperlyformatted queries, or models are generated that are incapable of beingused.

Accordingly, various aspects provide systems and methods to alleviatethe difficulty of query generation. According to one embodiment, thesystems and methods provide an intuitive user interface that enablesautomatic specification of queries and constraints. Variousimplementations provide novel methodologies for automaticallyformulating machine learning and optimization queries. The automaticgeneration of ML and/or optimization queries can be configured to useexamples to facilitate formulation of ML and optimization queries.

According to one aspect, a computer implemented method for automaticallygenerating machine learning problems is provided. The method comprisesaccepting, by a computer system, input data specifying variables anddata values associated with the variables, identifying, by the computersystem, at least one unspecified data record within the input data,determining, automatically, by the computer system, a relationshipbetween the variables specified in the input data and a variableassociated with the at least one unspecified data record, based ontraining data contained within the input data, and generating,automatically, by the computer system a machine learning problemincluding the relationship between the variables and the training data.

According to one embodiment, the method further comprises solving,automatically by the computer system, the machine learning problem toprovide a result value for the at least one unspecified data recordwithin the input data. According to one embodiment, the method furthercomprises assigning a function for calculating the result value to theat least one unspecified data record. According to one embodiment, themethod further comprises displaying, by the computer system the machinelearning problem. According to one embodiment, accepting the input dataincludes accepting within a spreadsheet display of variables and datavalues selection of a group of the variables, the data values, and theat least one unspecified data record.

According to one embodiment, identifying, determining, and generatingoccur in response to selection of the group. According to oneembodiment, the method further comprises determining a confidence valueassociated with the result value. According to one embodiment, themethod further comprises encoding, visually, the confidence levelassociated with the result value. According to one embodiment, encodingvisually the confidence level associated with the result value includesdisplaying a value having a high relative confidence value bolder than avalue having a lower relative confidence value. According to oneembodiment, the method further comprises converting categorical valuesfrom the input data into set membership binary values.

According to one embodiment, generating, automatically, by the computersystem the machine learning problem including the relationship betweenthe variables and the training data, includes generating the machinelearning problem based on symbolic regression. According to oneembodiment, generating, automatically, by the computer system, themachine learning problem includes: generating one or more possible querypatterns that relate a blank or otherwise identified cell in aspreadsheet to its surrounding non-blank cells, collecting data from thespreadsheet that matches that pattern, running a ML algorithm to find amodel and determine its confidence level, and selecting the model havingthe highest confidence level.

According to one embodiment, the method further comprises filling invalues in all blank cells that fit the query pattern using the selectedmodel with the highest confidence level. According to one embodiment,generating one or more possible query patterns includes generatingpossible patterns including both absolute cell positions and cellpositions that are relative to the at least one unspecified data recordin a spreadsheet. According to one embodiment, the method furthercomprises accepting at least one new unspecified data record, andcalculating a result value for the at least one new unspecified datarecord according to the input data and the result value for the at leastone unspecified data record.

According to one embodiment, generating, automatically, by the computersystem the machine learning problem including the relationship betweenthe variables and the training data includes automatically formulatingprediction queries to fill in the at least one unspecified data record.According to one embodiment, the at least one unspecified data record isat least one blank cell in a spreadsheet, and wherein the predictionquery is generated based on the relationship between the at least oneblank cell and cells having data values. According to one embodiment,generating, automatically, by the computer system the machine learningproblem including the relationship between the variables and thetraining data includes automatically formulating at least oneoptimization query to fill in the at least one unspecified data record.According to one embodiment, the at least one unspecified data record isat least one blank cell in a spreadsheet, and wherein the optimizationquery is generated based on the relationship between the at least oneblank cell and cells having data values.

According to one embodiment, generating, automatically, by the computersystem a machine learning problem including the relationship between thevariables and the training data includes: generating a model thatrelates a target value to the blank value; and performing a search toidentify the optimal value of the blank cells that minimize thedifference between the target value and the value predicted by themodel. According to one embodiment, the method further comprises holdingconstant at least some of the non-blank cells corresponding toindependent variables as search constraints. According to oneembodiment, multiple target values are optimized simultaneously using amulti-objective optimization technique. According to one embodiment, themethod further comprises accepting constraints defined against the atleast one unspecified data record.

According to one aspect, a system for automatically generating machinelearning problems is provided. The system comprises at least oneprocessor operatively connect to a memory, the at least one processorwhen executing is configured to: accept input data specifying variablesand data values associated with the variables, identify at least oneunspecified data record within the input data, determine, automatically,a relationship between the variables specified in the input data and avariable associated with the at least one unspecified data record, basedon training data contained within the input data, and generate,automatically, a machine learning problem including the relationshipbetween the variables and the training data.

According to one embodiment, the at least one processor is configured tosolve, automatically, the machine learning problem to provide a resultvalue for the at least one unspecified data record within the inputdata. According to one embodiment, the at least one processor isconfigured to assign a function for calculating the result value to theat least one unspecified data record. According to one embodiment, theat least one processor is configured to display the machine learningproblem. According to one embodiment, accepting the input data includesaccepting within a spreadsheet display of variables and data values aselection of a group of the variables, the data values, and the at leastone unspecified data record.

According to one embodiment, identifying, determining, and generatingoccur in response to selection of the group. According to oneembodiment, the at least one processor is configured to determine aconfidence value associated with the result value. According to oneembodiment, the at least one processor is configured to encode,visually, the confidence level associated with the result value.According to one embodiment, encoding visually the confidence levelassociated with the result value includes displaying a value having ahigh relative confidence value bolder than a value having a lowerrelative confidence value.

According to one embodiment, the at least one processor is configured toconvert categorical values from the input data into set membershipbinary values. According to one embodiment, the at least one processoris configured to generate the machine learning problem based on symbolicregression. According to one embodiment, the at least one processor isconfigured to generate one or more possible query patterns that relate ablank cell in a spreadsheet to its surrounding non-blank cells, collectdata from the spreadsheet that matches that pattern, execute a MLalgorithm to find a model and determine its confidence level, and selectthe model having the highest confidence level.

According to one embodiment, wherein the at least one processor isconfigured to fill in values in all blank cells that fit the querypattern using the selected model with the highest confidence level.According to one embodiment, the at least one processor is configured togenerate possible patterns including both absolute cell positions andcell positions that are relative to the at least one unspecified datarecord in a spreadsheet. According to one embodiment, the at least oneprocessor is configured to accept at least one new unspecified datarecord, and calculating a result value for the at least one newunspecified data record according to the input data and the result valuefor the at least one unspecified data record.

According to one embodiment, the at least one processor is configured toautomatically formulate prediction queries to fill in the at least oneunspecified data record. According to one embodiment, the at least oneunspecified data record is at least one blank cell in a spreadsheet, andwherein the prediction query is generated based on the relationshipbetween the at least one blank cell and cells having data values.According to one embodiment, the at least one processor is configured toautomatically formulate at least one optimization query to fill in theat least one unspecified data record.

According to one embodiment, the at least one unspecified data record isat least one blank cell in a spreadsheet, and wherein the optimizationquery is generated based on the relationship between the at least oneblank cell and cells having data values. According to one embodiment,the at least one processor is configured to generate a model thatrelates a target value to the blank value; and perform a search toidentify the optimal value of the blank cells that minimize thedifference between the target value and the value predicted by themodel.

According to one embodiment, the at least one processor is configured tohold constant at least some of the non-blank cells corresponding toindependent variables as search constraints for optimization. Accordingto one embodiment, the at least one processor is configured to optimizemultiple target values simultaneously using a multi-objectiveoptimization technique. According to one embodiment, the at least oneprocessor is configured to accept constraints defined against the atleast one unspecified data record.

Still other aspects, embodiments, and advantages of these exemplaryaspects and embodiments, are discussed in detail below. Any embodimentdisclosed herein may be combined with any other embodiment in any mannerconsistent with at least one of the objects, aims, and needs disclosedherein, and references to “an embodiment,” “some embodiments,” “analternate embodiment,” “various embodiments,” “one embodiment” or thelike are not necessarily mutually exclusive and are intended to indicatethat a particular feature, structure, or characteristic described inconnection with the embodiment may be included in at least oneembodiment. The appearances of such terms herein are not necessarily allreferring to the same embodiment. The accompanying drawings are includedto provide illustration and a further understanding of the variousaspects and embodiments, and are incorporated in and constitute a partof this specification. The drawings, together with the remainder of thespecification, serve to explain principles and operations of thedescribed and claimed aspects and embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of at least one embodiment are discussed below withreference to the accompanying figures, which are not intended to bedrawn to scale. Where technical features in the figures, detaileddescription or any claim are followed by reference signs, the referencesigns have been included for the sole purpose of increasing theintelligibility of the figures, detailed description, and claims.Accordingly, neither the reference signs nor their absence are intendedto have any limiting effect on the scope of any claim elements. In thefigures, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in every figure.The figures are provided for the purposes of illustration andexplanation and are not intended as a definition of the limits of theinvention. In the figures:

FIG. 1 is a block diagram of an auto-query system, according to oneembodiment;

FIGS. 2A-C are example user interface displays, according to oneembodiment;

FIG. 3 is an example process flow for generating an ML problem,according to one embodiment;

FIG. 4 is an example process flow for generating ML queries and/oroptimization searches in response to user input, according to oneembodiment;

FIGS. 5A-B are example user interfaces illustrating ML problemdefinition, according to one embodiment;

FIG. 6 is an example user interface illustrating ML problem definition,according to one embodiment;

FIGS. 7A-B are example user interfaces illustrating ML problemdefinition, according to one embodiment;

FIG. 8 is an example user interface illustrating ML problem definition,according to one embodiment; and

FIG. 9 is a block diagram of one example of a computer system that maybe used to perform processes and functions disclosed herein; and

FIG. 10 is a visual representation of one query pattern capture process,according to one embodiment; and

FIG. 11 is a visual representation of query pattern recognition,according to one embodiment.

DETAILED DESCRIPTION

As described above, systems and methods are provided that implement anauto-query process for implementing ML discovery. Various embodiments ofthe auto-query systems and methods enable users to describe a query forML discovery indirectly. For example, the system can provide userinterface displays that enable selection of data on which to model andspecification of values to solve without requiring specification of thequery directly. In one example, the system provides user interfaces forinteracting with data displayed as a spreadsheet. The user is able tospecify values on which they want solutions by providing blank cellswhere information needs to be predicted or optimized or combinations ofboth.

According to one embodiment, an auto-query engine is configured toformulate the ML modeling query or optimization search automaticallybased on the data selected in the display and, for example, the blankcells within that data. The auto-query engine can be configured togenerate the ML modeling query or optimization search automatically tobest fill in blank data records. According to some embodiments, theengine can analyze data provided by a user to determine the nature ofthe solution being requested. Depending on the arrangements of datacells and blank cells, the auto-query system is then configured toautomatically formulate prediction queries or optimization queries, andgenerate results accordingly. The automatically generated results can beplaced into the formerly blank cells. In some examples, the userinterface displays are configured to highlight the generated results andtheir respective displays to bring to the user's attention the resultsof the system's operations.

Showing in FIG. 1 is a block diagram of an example auto-query system100. System 100 can include an auto-query engine 104 for generating MLproblems, including ML queries and/or optimization searches,automatically. According to some embodiments, the auto-query engineaccepts a data input 102, analyzes the data input to determine a queryand/or optimization search that best fits the input data 102. In oneexample, the auto-query engine 104 executes a generated query on theinput data 102 to automatically provide predictions of results 106. Inanother example, the auto-query engine 104 executes a generatedoptimization search against the input data 102 to automatically provideresults 106 for unknown data records. In further examples, multiple datamodels may be required to provide solutions for a data set, thusrequiring formulation of multiple queries to generate results.

According to another embodiment, the engine 104 can also be configuredto test for specific data relationships as part of query and/oroptimization search building. For example, time series predictionproblems and/or other sequence solving problems can be automaticallydetected where dependencies exits in the data (e.g., 102), not justacross absolute position but also relative relationships (e.g., betweeneach column of data and preceding data for variables in other columns).The engine 104 can be configured to derive relative models for an inputdata (e.g., 102) and if determined with confidence, can be used to makeprediction for results (e.g., 106). In one example, the engine 104 cangenerate time series predictions (e.g., FIG. 6, shows a times seriesdata set and prediction values, discussed in greater detail below).

According to one embodiment, system 100 and/or its elements (e.g.,auto-query engine 104) can be provided using a computing system such asthe computer system 600 and/or 602 described with reference to FIG. 6.According to one aspect, the system 100 and/or the auto-query engine 104can be configured to generate, automatically, ML based queries and/oroptimization searches, execute the query and/or optimization, and returnresults 106 for unknowns within the provided data. In some embodiments,the system 100 and/or auto-query engine 104 is configured to acceptselection of data records in a table (e.g., highlighting of cells in anexcel sheet) to specify a data input 102 from which the system 100and/or engine 104 provides ML solutions for any non-specified datacells.

In further embodiments, the system 100 and/or auto-query engine 104 isconfigured to accept definition of values in conjunction with the inputdata to constrain the results returned. In one embodiment, the systemand/or engine optimizes the results returned according to any definedconstraints.

According to another aspect, even the most novice of computer users areeasily capable of selection of data records in a table, thus, even themost novice of computer users are enabled to execute sophisticatedmachine learning techniques and tools to derive powerful predictions andoptimization solutions. According to further aspects, returned resultsare visually encoded in a user interface display to the end user. Thevisual encoding can be configured to convey, intuitively, information onthe results generated through machine learning based solutions. Forexample, prediction and/or optimization solutions can be associated witha confidence level. The confidence level provides information on thedegree to which a model represents the data and/or the degree of errorassociated with the results (e.g., 106). Each result can be displayedaccording to an associated level of confidence. In one example, lowconfidence results are displayed “grayed-out” to demonstrate the lowlevel of certainty with respect to the value. The system and/or engine104 can be configured to provide varying degrees of highlighting toreflect higher levels of confidence. For example, the more pronouncedthe highlighting, the greater the level of confidence in a particularresult.

According to one embodiment, the auto-query engine 104 can includevarious system components that perform specific ones or combinations ofthe functions described. For example, the auto-query engine 104 caninclude an analysis component 108. According to one embodiment, theanalysis component 108 is configured to analyze data communicated tosystem 100 and/or engine 104. The analysis component can analyze thedata (e.g., 102) to determine properties of the input information. Forexample, the analysis component 108 can be configured to identifyheadings for data, determine variables based on data positioning,identify unspecified records for result generation, determine anyconstraints specified (e.g., by a user), among other options. In someembodiments, the analysis component 108 can be configured to identifymultiple model problems (e.g., nested prediction problems where multiplemodels need to be generated to specify multiple queries), times seriesprediction problems, optimization searches, and, for example, apply anydefined constraints to optimization searching.

According to another embodiment, the analysis component 108 can beconfigured to generate query patterns for data provided (e.g., 102). Theanalysis component 108 is configured to identify training data from thedata provided that matches the query pattern and use that training datato determine one or more data models for solving the query. Shown inFIG. 2A is an example user interface display 200 including a data setsupplied as an excel table (e.g., 202), having variable names at row204, data records to solve (at cells 206). The analysis componentidentifies the training data at 202 for the data set.

The analysis component 108 is configured to generate a model of theinput data. In the example shown, the analysis component 108 determinesa relationship between the variables of the data set for the model.Based on the determined relationship(s), the analysis componentgenerates a query containing the training data and an ML problem (e.g.,unspecific data=a function of specified data). The analysis componentcan be configured to execute a variety of approaches for determining aquery for the data set provided. In one embodiment, the analysiscomponent is configured to execute symbolic regression techniques todetermine models and/or relationships for data. The regressionapproaches executed can include co-evolutionary symbolic regression.Co-pending U.S. patent application Ser. No. 14/016,287 entitled “SYSTEMSAND METHODS FOR DATA SET SUBMISSION, SEARCHING AND RETRIEVAL,” and Ser.No. 14/016,300 entitled “SYSTEMS AND METHODS FOR SYMBOLIC ANALYSIS”describe some approaches for data modeling that can be used inconjunction with embodiments of the present disclosure, which areincorporate by reference herein in their entirety. Additional modelingapproaches can be executed by the analysis component, including forexample, neural networks, support vector machines, decision trees,symbolic regression, and other techniques. The analysis component 108can be configured to execute one or more of these techniques to create amathematical model that can be applied, for example, to predictdependent values from new independent variables, based on examples(training data—e.g., 102).

According to one embodiment, the auto-query engine 104 can also includea user interface component (“UI component) 110 that is configured togenerate and/or modify displays shown to an end-user responsive tooperations of other components (including e.g., analysis component 108).According to one embodiment, the UI component can be configured togenerate displays to an end-user that enable the user, for example, tocut and paste data (e.g., 102) into spreadsheet displays. The UIcomponent 110 can pass the data input to the analysis component 108 forprocessing. In some embodiments, the UI component 110 can providefunctions for uploading data (e.g., in spreadsheet format, csv format,tabular format, etc.). In some embodiments, the UI component 110 can beconfigured to require users to enter data according to specific formatsand provide details to an inputting user on the formats in respectiveuser interface displays (e.g., variable name headers with data for thevariable in a respective column or row). In another example, the userinterface can accept data without variable definitions. The userinterface can be configured to indicate, in response to data without anyvariable designation, that the data set will be evaluated as unordereddata.

According to one embodiment, once the data is accepted the UI component110 can provide the data to the analysis component 108. The analysiscomponent can be configured to deliver the results of its operations tothe UI component 110 for display. For example, in FIG. 2A the analysiscomponent provides information on the problem being solved to the UIcomponent 110, which is displayed at 208 (“Estimate”—reflecting aprediction of a dependent variable from the data in independentvariables). Shown in FIG. 2B is an example user interface display 250.Display 250 shows a generated query at 252. The generated query includesfor example training data at 254 and the relationship between thevariables 256 to be solved using the training data.

In some embodiments, the UI component can be configured to acceptspecification operations to define the problem to be solved. In oneexample, the UI component 110 can accept specification of maximize,minimize, limit, etc., with respect to data cells and/or values. Theidentification of maximize, minimize, limit, etc., indicates a problemto optimize solutions around the specified constraint. In anotherexample, the UI component 110 can also be configured to accept a targetvalue in a data set without any further specification. The analysiscomponent 108 can also be configured to identify such target values(e.g. FIG. 8 at 806) and determine an optimization search to meet thetarget.

According to another embodiment, the auto-query engine 104 can alsoinclude a result component 112 configured to execute any query and/oroptimization search generated by the analysis component (e.g., 252 ofFIG. 2B). In some implementations, the operations described with respectto the result component 112 can be executed by the analysis component108, and the auto-query engine 104 can execute with the analysis and UIcomponents alone. In other implementations, the result component 112 canalso be a sub-component of the analysis component 108.

The result component 112 can be configured to execute a variety ofapproaches for determining results from the query and/or optimizationsearch generated for a data set. In one embodiment, the result componentis configured to execute symbolic regression techniques to determineresults for a query. The regression approaches executed can includeco-evolutionary symbolic regression. Additional approaches fordetermining results include neural networks, support vector machines,decision trees, and other techniques. The result component 112 can beconfigured to execute one or more of these techniques to create a resultfor each unspecified data record in a data set.

As is known, ML solutions are associated with confidence levelsreflecting how well an ML approach was able to model and/or fit a set ofdata. Each result can be associated with a different confidence level,determined, for example, by the result component 112. The confidencelevel can be determined as the results are being generated or postgeneration, using for example, a portion of the training data heldseparately for validation. As discussed above, the results of operationsfrom respective components can be provided to the UI component 110 toenhance displays to an end-user. According to one embodiment, eachresult determined for a data set can be visually encoded with itsrespective confidence level. In one example, a display of the data isbolded and/or grayed-out to reflect varying degrees of confidence. Avalue having a high confidence level can be displayed in bold, while avalue having low confidence level can be displayed grayed-out. In oneexample, intensity of the display of a value corresponds to a confidencelevel. Shown in FIG. 2C is an example user interface display 270.Display 270 shows a set of results at 272, where each result has anassociated visual encoding reflective of a respective confidence level.

According to various embodiments, system 100, engine 104, and/orrespective components can execute a variety of process to define MLproblems automatically from data sets and return results to theautomatically defined problems. Shown in FIG. 3 is an example process300 for automatically defining an ML problem. The process 300 begins at302 with analysis of a data set. In one example, the data set can besupplied by an end user through a user interface. The user interface canbe a local display from a locally executed program. In some examples,the program can be locally resident on the computer machine, executedfrom a browser, and/or downloaded for execution. In one example, theinterface can be generated from a website or portal provided over acommunication network (e.g., the Internet).

Analysis of the data set at 302 can include determining the type ofproblem being requested (e.g., prediction of values, optimizationsearch, nested prediction, time series prediction, or combinationsthereof). According to one embodiment, the data set is analyzed at 302to identify data on which to model (i.e., training data), values tosolve, etc. At 304, relationships within the data are determined usingML techniques (e.g., symbolic regression, etc.). The determinedrelationships are used at 306 to generate automatically an ML problem tobe solved. The ML problem to be solved can include a query specifyingtraining data to employ and a relationship between the variables withinthe training data (e.g. shown at 252 of FIG. 2B). Once the ML problem tobe solved is generated, the specification of the ML problem can bedisplayed, for example, to an end user.

According to some embodiments, process 300 can optionally include stepsfor solving the generated ML problem, for example, at 308. Once resultsare generated, the results can also be displayed to an end user.

In some embodiments, a user may interact with user interface displays todefine the data to be analyzed and provide any constraints the user maywish to specify for generating solutions. Shown in FIG. 4 is an exampleprocess 400 for generating ML queries and/or optimization searches inresponse to user input. Process 400 begins at 402 within generation of adata entry display. The data entry display can accept user input todefine data fields, including, for example, definition of variable namesand values for each variable. Further, the data entry display enablesspecification of data values to be solved. In one example, providingblank records within a spreadsheet display in association with somedefined values causes calculation of results for the blank records.

According to one embodiment, process 400 continues with selection ofdata records within spreadsheet display at 404. Selection can include adrag operation with a mouse to highlight data within a user interface.In some other examples, depressing a control key plus clicking with amouse on data records enables data selection at 404. In other examples,shift select and other known operations can also be used to highlightdata records on which to operate.

Once the data is defined, a query and/or optimization search isgenerated based on evaluation of the selection data at 406. In oneexample, generation of a query takes place at 406 based on adetermination that the data includes independent variables andunspecified values for dependent variables. A query is generated,automatically, specifying a relationship between the variables andtraining data on which to solve at 406. In another example, values forindependent variables can also be solved by first generating a model topredict dependent values and then using the model to search for valuesfor the independent variables. The search can be constrained, forexample, to maximize, minimize, or approach a target value, thusoptimizing a particular result according to any constraints. Once the MLproblem is specified (including, e.g., query/prediction oroptimization), various known ML approaches are executed at 408 to solvethe problem specified at 406. At 410, the results of the solution aredisplayed to the user in the user interface. In some embodiments, theset of results can be displayed with an associated confidence level. Forexample, each result can be highlighted or de-emphasized according torespective confidence levels associated with the determined result.

Various user interface displays can be generated during execution ofprocess 400, for example, by a UI component 110. Shown below are varioususer interface displays for interacting with data sets, automaticallygenerating ML problems, and generating solutions to the problems fordisplay to users. According to some embodiments, process 300 and/or 400can be executed to generate the displays shown. Additionally otherprocesses can be executed (e.g., by an auto-query system 100 and/orengine 104), to generate the user interface displays described. Infurther embodiments, the operations discussed with respect to process300 and/or 400 can be executed in different order, can be combined intofewer steps, among other options, for example to provide displays ofautomatically generated queries, optimization searches, time seriespredictions, among other options.

FIGS. 2A-C illustrate example interface displays for material propertypredictions. The dataset shown in 200 can be provided by a user in theform of a spreadsheet, where each column is assigned a variable name(pressure 210, temp 212, flow rate 214, or strength 216 of resultingmaterial). The three first columns are independent variables, and thefourth column is the dependent variable (e.g., as determined by ananalysis component 108) that represents the strength of the materialproduced by a hypothetical manufacturing plant with independent controlsover pressure, temperature and flow rate.

According to one embodiment, the top eight rows 202 represent datacollected from eight experiments. The bottom six rows 206 representpotential situations for which the user wants to know an expected value.The auto-query system is configured to automatically formulate the queryshown in the inset box 252 of FIG. 2B. The query 252 contains both atraining dataset 254 (comprising the first eight rows) and an ML queryin the form y=f(x) at 256 (e.g., Strength=f(Pressure, Temp, Flow). Thesystem executes the ML query and the resulting model is used to fill inthe blank cells (e.g., at 272 of FIG. 2C).

FIG. 5A illustrates another example user interface 500 for a materialproperty prediction. The problem shown in 500 requires that the systemdevelop multiple models and hence multiple queries to provide results.The system (e.g., 100) can be configured to identify “nested model”problems. According to some embodiments, the system can be configured toorder nested queries according to the confidence level of the results.In one example, the system implements a dynamic programming approach togenerate nested queries and evaluate corresponding confidence levels.Models with more confidence (i.e., less error) are preferred over moreuncertain models when choosing between multiple possible pathways for aprediction. Multiple models are generated in all orders possible, andthe resulting models are weaned out based on confidence levels.

Shown in FIG. 5A, the leftmost query 520 is generated defining atraining data set 522 and a relationship between the variables on whichto solve 524. The rightmost query 540 is likewise generated having itsown training data 542 and relationship 544 for solving. Based on thedynamic programming evaluation of confidence, the left side query 520 isissued before the right-side query 540. As shown, the results from theleft queries are then used as inputs to define solutions according tothe right side model. In some embodiments, the system (e.g., 100) and/orengine (e.g., 104) is configured to execute the well-known dynamicprogramming approach to preparing and ordering execution of queries togenerate solutions for a data set. The dynamic programming approachemploys a “divide and conquer” approach, where sub-problems are solvedfirst and the larger problems make use of solutions to sub-problems.

Shown in FIG. 5B is another example display 550. Display 550 illustratean example nested solution and associated results, where the results forthe left side query are shown at 552 and the results for the right sidequery at 554. As discussed, respective confidence levels can beillustrated at 552 and 554 respectively. In one example, the more grayedout a solution appears in the display the lower the confidence levelassociated with the respective solution. In addition to confidence leveldisplays, the system can be configured to provide contextual informationfor a given solution. At 556 and 558, the system can provide displays(e.g., through a UI component) configured to describe the model used togenerate the results and any reasoning from the model analysis. In thissetting, provided are the functions derived for the left side query 558and an indicator regarding the confidence levels of the results whichcan be displayed upon selection of “Why” at 556. According to oneexample, the “Why” display at 556, is configured to facilitateunderstanding of the confidence levels and model associated with thegenerated results.

According to other embodiments, the system and/or engine (e.g., 100and/or 104) can also be configured to identify dependencies within datasets not just across absolution position (e.g., rows 618-24 of FIG. 6)but also relative relationships (e.g., between each column 602-616). Inthis example, the system detects the relative relationship between acolumn and its two preceding columns to the left by generating datamodels on the input data. According to one embodiment, the system isconfigured to identify and automatically attempt modeling/resultgeneration on the basis of relative relationships within the data. Ifappropriate models and/or results can be found with confidence, theresulting predictions are provided by the system, for example, as timeseries predictions at 626 and 628 in display 600.

According to some embodiments, similar displays can provide forspecification of optimization solutions. Shown in FIG. 7A is an exampledisplay 700. Display 700 is configured to accept specification of atarget value for optimizing solutions. In an optimization situation, theindependent variables are left blank and the target value is provided(e.g., at 702). In some embodiments, the optimization request can comeas part of an interaction session between the system and a user. Forexample, the user can have requested prediction scenarios to generateresults at 704. Additionally nested prediction scenarios can have beenexecuted on the system to derive results at 706. In one alternative, theuser may also provide training data and input an optimization value(e.g., at 702) to cause the system to generate optimization solutionswithout other preceding solutions being required.

According to one embodiment, the system (e.g., 100) and/or engine (e.g.,104) formulates a model to predict the dependent values from theindependent values, according to training data identified (e.g., at 708and in another example using other predictions at 710). The systemand/or engine uses the model to search for the best values for theindependent variables 712 so that outcome from the model matches thespecified target dependent value or in another example, gets close toit.

Shown in FIG. 7B is an example display 750 with a display of resultscalculated according to an optimization value entered at 754. Thedisplay of the results at 752 provide the values determined to meet,exceed, and/or come as close to the optimization value as possible. Insome examples, a background highlight may provide a visual indicationthat an optimization value could not be achieved. In some examples, ifan optimization value could not be achieved, the best possible value canbe provided with the values for the independent variables that generatedthe best possible value.

FIG. 7A illustrates a maximization problem specified using a high targetvalue (e.g., 10 at 702). The system can also be configured to accept alow target value (e.g., 0) to specify a minimization problem. In anotherexample, the system can also process an arbitrary value and use thearbitrary value to specify a target optimization problem, such that theresults provided deliver the target value or as close to the targetvalue as possible.

FIG. 8 illustrates another display 800 in another solution scenario.Display 800 illustrates a constrained optimization scenario. In thisexample, the user has specified a minimization problem by entering “0”at 802. In one embodiment, the system is configured to determine thebest values for the independent values at 804 to meet the minimizationrequest by the user. However, in this example, the user has alsospecified a value at 806. According to one embodiment, the system isconfigured to recognize when some of the independent variables arespecified, and others are not. The system recognizes this scenario as aconstrained optimization problem. In response, the system is configuredto optimize only the values of the unspecified independent variables(e.g., 808 and 810).

According to various embodiments, the display provided to end users caninclude a number of features that are configured to enable even noviceusers the ability to interact with and understand the nature of theresults being returned. For example, a UI component of the system can beconfigured to enable users to formulate ML problem (e.g., queries) bydragging a selection box (e.g., 211 of FIG. 2, 510 of FIG. 5, etc.) overboth the data and the blank query cells. In some examples, the UIcomponent can provide an activity button in user interface displays.Once the set of data is selected the user can click an activity button(e.g., 208 of FIG. 2) to generate a set of results. The activity buttoncan be labeled “Estimate” or “fill in the blanks,” among other options.In addition, a menu option can also be provided to begin processing, andin another example, the user can right click the selection box to accessanother menu including an “Estimate” or “Fill in the Blanks” option.

As discussed, each display of a set of results can include visualencoding of the results. In one example, the grayscale color of thecells filled in represents the uncertainty of the value computed, wherebold colors represent more confidence, and blander colors represent lessconfidence.

According to some embodiments, rather than provide results as values invarious spreadsheet displays, the system, engine, and/or UI component isconfigured to provide the formula to calculate the result value as anentry in a spreadsheet. According to some embodiments, providing theformula rather than a constant, results in improvements. In one example,the displayed predicted/optimized value is configured to adjustautomatically in response to changes made in the values of theindependent variables. According to some embodiments, the system can beconfigured to analyze the modification of values according to theprovided formulas to maintain determinations of confidence with respectto a displayed value. In some applications the brightness of the valuemay also change if the confidence of the prediction/optimizationchanges.

In another example, providing the formula (e.g., by system 100, engine104, and/or UI component 110) enables the user to examine the formula togain insight into the relationship found.

Shown in FIG. 10 is a visual representation of one query pattern captureprocess, according to one embodiment. In one example, in order to issuea query for modeling or optimization, the system first generates apattern that contains slots for independent values (represented here asx) and slots for dependent values (represented here as y). The examplepatterns are defined by the system during analysis of the data shown inFIG. 10. The system can be configured to apply each pattern to the datagrid at multiple locations, and even multiple patterns for overlappinglocations. When both the independent and dependent slots in the patternare filled with values, the pattern serves for collecting data. Whenonly the slots labeled x are filled with values but the slots with y areblanks, the pattern identifies a place where the result from the queryy=f(x) can be applied.

In various executions, many potential patterns will be identified forfilling each blank cell, and some executions multiple patterns can beused to fill in multiple cells. For example, the “A” template can beused to collect 11 data points and to fill in three blanks. The “B”template can be used to collect data from 8 locations and can be used tofill in six blank slots. The “C” template can be used to collect datafrom 7 locations and can be used to fill in two blanks. The “D” templatecan be used to collect data from eight locations and can be used tocomplete the values of six blank cells. Each template therefore hasdifferent usefulness depending on how much data it can collect, how manyblanks it can fill, and what confidence level the corresponding ML modelwill provide.

In some executions, the system is configured to identify the highestconfidence template for use with each blank cell. Generally, theconfidence of the model generated by the system depends on the amount ofdata collected by the template and the complexity of the underlyingrelationship between x and y in the template, if one exists.

According to some embodiments, it is not always possible to try out allpossible templates. Some algorithms implemented by the system areconfigured to try out a subset of all possible templates, noting thatsome templates are contained within other templates. The system can beconfigured to leverage the property that smaller templates willgenerally provide more data and simpler models, whereas larger modelsmay entail more variables collect less data. The system balances thisproperty against the property that larger templates make less a-prioriassumptions about the structure of the relationships. Thus, in someembodiments, the large templates can be used to first identify coarserelationships, and those identified coarse relationships used to guidethe selection of narrower models to generate more refined queries. Forexample, the “C” template is contained within the “D” template. The “C”template is therefore more specific than the “D” template. However, the“D” template can be used first by the system and if the resulting modelonly uses the leftmost two of the three dependent variables, the “C”template is analyze by the system. Blanks in column 4 rows 10-12 arefilled in using the model resulting from the “D” or “C”, whichever hashigher confidence. In one example, the blanks in the last three rows ofcolumn 4 cannot use the “D” template, so the system selects the “C”template. FIG. 11 is a visual representation of one example of querypattern recognition, according to one embodiment.

1102A is an example of one pattern useful for filling in blacks in thethird column. This pattern could be used to collect data from rows whereall cells are provided (for example row 2&3) and also used to formulatea query when some of the cells are missing (row 15). 1104B is an exampleof another pattern useful for filling in blanks in the fourth column.This pattern could be used to collect data from rows where all cells areprovided (for example rows 5, 6 & 7) and also to formulate a query whensome of the cells are missing (rows 9, 10 & 11). 1106C is an example ofan alternative pattern useful for filling in blanks in the fourthcolumn. This pattern could be used to collect data from rows where cellsare provided (for example row 3) and also used to formulate a query whensome of the cells are missing (row 13, 14, and 15). 1108D is an exampleof another pattern (Rows 8 & 12).

Example Computer Implementations

Various aspects and functions described herein, in accord with aspectsof the present invention, may be implemented as hardware, software, or acombination of hardware and software on one or more computer systems.There are many examples of computer systems currently in use. Someexamples include, among others, network appliances, personal computers,workstations, mainframes, networked clients, servers, media servers,application servers, database servers, web servers, and virtual servers.Other examples of computer systems may include mobile computing devices,such as cellular phones and personal digital assistants, and networkequipment, such as load balancers, routers and switches. Additionally,aspects in accord with the present invention may be located on a singlecomputer system or may be distributed among one or more computer systemsconnected to one or more communication networks.

For example, various aspects and functions may be distributed among oneor more computer systems configured to provide a service to one or moreclient computers, or to perform an overall task as part of a distributedsystem. Additionally, aspects may be performed on a client-server ormulti-tier system that includes components distributed among one or moreserver systems that perform various functions. Thus, the invention isnot limited to executing on any particular system or group of systems.Further, aspects may be implemented in software, hardware or firmware,or any combination thereof. Thus, aspects in accord with the presentinvention may be implemented within methods, acts, systems, systemplacements and components using a variety of hardware and softwareconfigurations, and the implementation is not limited to any particulardistributed architecture, network, or communication protocol.Furthermore, aspects in accord with the present invention may beimplemented as specially-programmed hardware and/or software.

FIG. 9 shows a block diagram of a distributed computer system 900, inwhich various aspects and functions in accord with the present inventionmay be practiced. The distributed computer system 900 may include one ormore computer systems. For example, as illustrated, the distributedcomputer system 900 includes three computer systems 902, 904 and 906. Asshown, the computer systems 902, 904 and 906 are interconnected by, andmay exchange data through, a communication network 908. The network 908may include any communication network through which computer systems mayexchange data. To exchange data via the network 908, the computersystems 902, 904, and 906 and the network 908 may use various methods,protocols and standards including, among others, token ring, Ethernet,Wireless Ethernet, Bluetooth, TCP/IP, UDP, HTTP, FTP, SNMP, SMS, MMS,SS7, JSON, XML, REST, SOAP, CORBA HOP, RMI, DCOM and Web Services.

Computer systems 902, 904 and 906 may include mobile devices such ascellular telephones. The communication network may further employ one ormore mobile access technologies including 2nd (2G), 3rd (3G), 4th (4G orLTE) generation radio access for cellular systems, WLAN, Wireless Router(WR) mesh, and other communication technologies. Access technologiessuch as 2G, 3G, 4G and LTE and future access networks may enable widearea coverage for mobile devices. For example, the network may enable aradio connection through a radio network access such as Global Systemfor Mobil communication (GSM), General Packet Radio Services (GPRS),Enhanced Data GSM Environment (EDGE), Wideband Code Division MultipleAccess (WCDMA), among other communication standards. Network may includeany wireless communication mechanism by which information may travelbetween the devices 904 and other computing devices in the network.

To ensure data transfer is secure, the computer systems 902, 904 and 906may transmit data via the network 908 using a variety of securitymeasures including TSL, SSL or VPN, among other security techniques.While the distributed computer system 900 illustrates three networkedcomputer systems, the distributed computer system 900 may include anynumber of computer systems, networked using any medium and communicationprotocol.

Various aspects and functions in accord with the present invention maybe implemented as specialized hardware or software executing in one ormore computer systems including the computer system 902 shown in FIG. 9.As depicted, the computer system 902 includes a processor 910, a memory912, a bus 914, an interface 916 and a storage system 918. The processor910, which may include one or more microprocessors or other types ofcontrollers, can perform a series of instructions that manipulate data.The processor 910 may be a well-known, commercially available processorsuch as an Intel Pentium, Intel Atom, ARM Processor, Motorola PowerPC,SGI MIPS, Sun UltraSPARC, or Hewlett-Packard PA-RISC processor, or maybe any other type of processor or controller as many other processorsand controllers are available. As shown, the processor 910 is connectedto other system placements, including a memory 912, by the bus 914.

The memory 912 may be used for storing programs and data duringoperation of the computer system 902. Thus, the memory 912 may be arelatively high performance, volatile, random access memory such as adynamic random access memory (DRAM) or static memory (SRAM). However,the memory 912 may include any device for storing data, such as a diskdrive or other non-volatile storage device, such as flash memory orphase-change memory (PCM). Various embodiments in accord with thepresent invention can organize the memory 912 into particularized and,in some cases, unique structures to perform the aspects and functionsdisclosed herein.

Components of the computer system 902 may be coupled by aninterconnection element such as the bus 914. The bus 914 may include oneor more physical busses (for example, busses between components that areintegrated within a same machine), and may include any communicationcoupling between system placements including specialized or standardcomputing bus technologies such as IDE, SCSI, PCI and InfiniBand. Thus,the bus 914 enables communications (for example, data and instructions)to be exchanged between system components of the computer system 902.

Computer system 902 also includes one or more interfaces 916 such asinput devices, output devices and combination input/output devices. Theinterface devices 916 may receive input, provide output, or both. Forexample, output devices may render information for externalpresentation. Input devices may accept information from externalsources. Examples of interface devices include, among others, keyboards,mouse devices, trackballs, microphones, touch screens, printing devices,display screens, speakers, network interface cards, etc. The interfacedevices 916 allow the computer system 902 to exchange information andcommunicate with external entities, such as users and other systems.

Storage system 918 may include a computer-readable andcomputer-writeable nonvolatile storage medium in which instructions arestored that define a program to be executed by the processor. Thestorage system 918 also may include information that is recorded, on orin, the medium, and this information may be processed by the program.More specifically, the information may be stored in one or more datastructures specifically configured to conserve storage space or increasedata exchange performance. The instructions may be persistently storedas encoded signals, and the instructions may cause a processor toperform any of the functions described herein. A medium that can be usedwith various embodiments may include, for example, optical disk,magnetic disk or flash memory, among others. In operation, the processor910 or some other controller may cause data to be read from thenonvolatile recording medium into another memory, such as the memory912, that allows for faster access to the information by the processor910 than does the storage medium included in the storage system 918. Thememory may be located in the storage system 918 or in the memory 912.The processor 910 may manipulate the data within the memory 912, andthen copy the data to the medium associated with the storage system 918after processing is completed. A variety of components may manage datamovement between the medium and the memory 912, and the invention is notlimited thereto.

Further, the invention is not limited to a particular memory system orstorage system. Although the computer system 902 is shown by way ofexample as one type of computer system upon which various aspects andfunctions in accord with the present invention may be practiced, aspectsof the invention are not limited to being implemented on the computersystem, shown in FIG. 9. Various aspects and functions in accord withthe present invention may be practiced on one or more computers havingdifferent architectures or components than that shown in FIG. 9. Forinstance, the computer system 902 may include specially-programmed,special-purpose hardware, such as for example, an application-specificintegrated circuit (ASIC) tailored to perform a particular operationdisclosed herein. Another embodiment may perform the same function usingseveral general-purpose computing devices running MAC OS System X withMotorola PowerPC processors and several specialized computing devicesrunning proprietary hardware and operating systems.

The computer system 902 may include an operating system that manages atleast a portion of the hardware placements included in computer system902. A processor or controller, such as processor 910, may execute anoperating system which may be, among others, a Windows-based operatingsystem (for example, Windows NT, Windows 2000/ME, Windows XP, Windows 7,Vista, or 8) available from the Microsoft Corporation, a MAC OS System Xoperating system available from Apple Computer, one of many Linux-basedoperating system distributions (for example, the Enterprise Linuxoperating system available from Red Hat Inc.), a Solaris operatingsystem available from Sun Microsystems, or a UNIX operating systemsavailable from various sources. Many other operating systems may beused, and embodiments are not limited to any particular operatingsystem.

The processor and operating system together define a computing platformfor which application programs in high-level programming languages maybe written. These component applications may be executable, intermediate(for example, C# or JAVA bytecode) or interpreted code which communicateover a communication network (for example, the Internet) using acommunication protocol (for example, TCP/IP). Similarly, functions inaccord with aspects of the present invention may be implemented using anobject-oriented programming language, such as SmallTalk, JAVA, C++, Ada,or C# (C-Sharp). Other object-oriented programming languages may also beused. Alternatively, procedural, scripting, or logical programminglanguages may be used.

Additionally, various functions in accord with aspects of the presentinvention may be implemented in a non-programmed environment (forexample, documents created in HTML, XML or other format that, whenviewed in a window of a browser program, render aspects of agraphical-user interface or perform other functions). Further, variousembodiments in accord with aspects of the present invention may beimplemented as programmed or non-programmed placements, or anycombination thereof. For example, a web page may be implemented usingHTML while a data object called from within the web page may be writtenin C++. Thus, the invention is not limited to a specific programminglanguage and any suitable programming language could also be used.

It is to be appreciated that embodiments of the methods and apparatusesdiscussed herein are not limited in application to the details ofconstruction and the arrangement of components set forth in thefollowing description or illustrated in the accompanying drawings. Themethods and apparatuses are capable of implementation in otherembodiments and of being practiced or of being carried out in variousways. Examples of specific implementations are provided herein forillustrative purposes only and are not intended to be limiting. Inparticular, acts, elements and features discussed in connection with anyone or more embodiments are not intended to be excluded from a similarrole in any other embodiments.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. Any references toembodiments or elements or acts of the systems and methods hereinreferred to in the singular may also embrace embodiments including aplurality of these elements, and any references in plural to anyembodiment or element or act herein may also embrace embodimentsincluding only a single element. References in the singular or pluralform are not intended to limit the presently disclosed systems ormethods, their components, acts, or elements. The use herein of“including,” “comprising,” “having,” “containing,” “involving,” andvariations thereof is meant to encompass the items listed thereafter andequivalents thereof as well as additional items. References to “or” maybe construed as inclusive so that any terms described using “or” mayindicate any of a single, more than one, and all of the described terms.Any references to front and back, left and right, top and bottom, upperand lower, and vertical and horizontal are intended for convenience ofdescription, not to limit the present systems and methods or theircomponents to any one positional or spatial orientation.

Having thus described several aspects of at least one embodiment of thisinvention, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those skilled inthe art. Such alterations, modifications, and improvements are intendedto be part of this disclosure, and are intended to be within the spiritand scope of the invention. Accordingly, the foregoing description anddrawings are by way of example only.

What is claimed is:
 1. A method, comprising: accessing, by a computersystem, time series data comprising a sequence of data records, each ofthe data records in the sequence corresponding to a respective time andindicating data values of a first variable and a second variable for therespective time; generating, by the computer system via application ofmachine learning to the sequence of data records, a function thatcomputes a data value of the first variable for a first time based atleast in part on respective data values of at least the second variablefor a plurality of times prior to the first time; and determining, bythe computer system using the function generated via application ofmachine learning to the sequence of data records, a data value of thefirst variable for a second time subsequent to one or more timescorresponding to any of the data records in the sequence.
 2. A system,comprising: one or more processors connected to memory to: access timeseries data comprising a sequence of data records, each of the datarecords in the sequence corresponding to a respective time andindicating data values of a first variable and a second variable for therespective time; generate, via application of machine learning to thesequence of data records, a function that computes a data value of thefirst variable for a first time based at least in part on respectivedata values of at least the second variable for a plurality of timesprior to the first time; and determine, using the function generated viaapplication of machine learning to the sequence of data records, a datavalue of the first variable for a second time subsequent to one or moretimes corresponding to any of the data records in the sequence.
 3. Themethod of claim 1, comprising: determining, by the computer system, aconfidence value associated with the data value of the first variablefor the second time subsequent to the one or more times determined usingthe function.
 4. The method of claim 1, comprising: converting, by thecomputer system, one or more values of the second variable in thesequence of data records into binary values.
 5. The method of claim 1,wherein the machine learning comprises symbolic regression, comprising:training, by the computer system, a model using the machine learning;and generating, by the computer system, the function using the model. 6.The method of claim 1, comprising: training, by the computer systemusing the machine learning, a plurality of candidate models for thefunction that computes the data value of the first variable for thefirst time and the respective data values of at least the secondvariable for the plurality of times prior to the first time.
 7. Themethod of claim 1, comprising: determining, by the computer system usingthe function and based at least on part on the data value of the firstvariable determined for the second time, a data value of the firstvariable for a third time subsequent to the second time.
 8. The methodof claim 1, comprising: providing, by the computer system, the functiongenerated via application of the machine learning for display via agraphical user interface.
 9. The system of claim 2, wherein the one ormore processors are further configured to determine a confidence valueassociated with the data value of the first variable for the second timesubsequent to the one or more times determined using the function. 10.The system of claim 2, wherein the one or more processors are furtherconfigured to convert one or more values of the second variable in thesequence of data records into binary values.
 11. The system of claim 2,wherein the machine learning comprises symbolic regression, and the oneor more processors are further configured to: train a model using themachine learning; and generate the function using the model.
 12. Thesystem of claim 2, wherein the one or more processors are furtherconfigured to train, using the machine learning, a plurality ofcandidate models for the function that computes the data value of thefirst variable for the first time and the respective data values of atleast the second variable for the plurality of times prior to the firsttime.
 13. The system of claim 2, wherein the one or more processors arefurther configured to determine, using the function and based at leaston part on the data value of the first variable determined for thesecond time, a data value of the first variable for a third timesubsequent to the second time.
 14. The system of claim 2, wherein theone or more processors are further configured to provide the functiongenerated via application of the machine learning for display via agraphical user interface.
 15. The method of claim 3, comprising:providing, by the computer system, an indication of the confidence valuefor display via a display device.
 16. The method of claim 3, comprising:providing, by the computer system, a visual indication of the confidencevalue that indicates a higher level of confidence relative to a lowerlevel of confidence associated with a second value.
 17. The method ofclaim 6, comprising: determining, by the computer system, respectiveconfidence levels associated with the plurality of candidate models; andselecting, by the computer system, a candidate model from the pluralityof candidate models based on the respective confidence levels to use togenerate the function.
 18. The system of claim 9, wherein the one ormore processors are further configured to provide an indication of theconfidence value for display via a display device.
 19. The system ofclaim 9, wherein the one or more processors are further configured toprovide a visual indication of the confidence value that indicates ahigher level of confidence relative to a lower level of confidenceassociated with a second value.
 20. The system of claim 12, wherein theone or more processors are further configured to: determine respectiveconfidence levels associated with the plurality of candidate models; andselect a candidate model from the plurality of candidate models based onthe respective confidence levels to use to generate the function.